Liu Y, Sun BJT, Zhang C, Li B, Yu XX, Du Y. Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model: A dual-center study. World J Gastroenterol 2024; 30(16): 2233-2248 [PMID: 38690027 DOI: 10.3748/wjg.v30.i16.2233]
Corresponding Author of This Article
Yong Du, MD, Professor, Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, No. 1 Maoyuannan Road, Nanchong 637000, Sichuan Province, China. duyong@nsmc.edu.cn
Research Domain of This Article
Medicine, Research & Experimental
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. Apr 28, 2024; 30(16): 2233-2248 Published online Apr 28, 2024. doi: 10.3748/wjg.v30.i16.2233
Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model: A dual-center study
Yan Liu, Bai-Jin-Tao Sun, Chuan Zhang, Bing Li, Xiao-Xuan Yu, Yong Du
Yan Liu, Bai-Jin-Tao Sun, Chuan Zhang, Bing Li, Xiao-Xuan Yu, Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
Yong Du, Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China. duyong@nsmc.edu.cn
Author contributions: Liu Y data acquisition and analysis, drafting and writing of the manuscript; Sun BJT data collection and data analysis; Zhang C, Li B and Yu XX language editing and revisions to the manuscript; Du Y work concept or design and important revisions to the manuscript; all authors have read and approve the final manuscript.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of the Affiliated Hospital of North Sichuan Medical College.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Open Access:
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial.
Corresponding author: Yong Du, MD, Professor, Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, No. 1 Maoyuannan Road, Nanchong 637000, Sichuan Province, China. duyong@nsmc.edu.cn
Received: January 2, 2024 Peer-review started: January 2, 2024 First decision: January 31, 2024 Revised: February 8, 2024 Accepted: March 20, 2024 Article in press: March 20, 2024 Published online: April 28, 2024 Processing time: 114 Days and 17.6 Hours
Core Tip
Core Tip: We constructed radiomics predictive models, clinical predictive model and clinical-radiomics (CR) model based on preoperative magnetic resonance imaging images of rectal cancer (RC), and independent clinical risk factors, to predict the preoperative perineural invasion (PNI) status of RC patients. The reliability and repeatability of the established predictive models were analyzed using internal and external validation groups. The CR model had the best stable neutral performance in both the internal and external validation groups. Therefore, the CR model was able to predict the PNI status of RC noninvasively before surgery, thereby providing support for the individualized treatment of RC patients.
